AI is a potential game-changer for the enterprise, but there’s an important prelude to success: the quality of the underlying data.

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Whether we’re ready for it or not, artificial intelligence (AI) is infiltrating the enterprise. From natural language processing systems that manage customer service inquiries to automated manufacturing plants staffed by robots, AI technologies, driven by machine learning, are having an impact. And with the accelerated rate of innovation—brought on by exponential increases in computer processing power and the sheer volume of data creation—AI clearly has the potential to transform just about every industry, from aerospace to retail.

In a Harvard Business Review article, MIT researchers Erik Brynjolfsson and Andrew McAfee labeled AI “the most important general-purpose technology of our era.” The effects of AI will be magnified in the coming decade, they say, as organizations “transform their core processes and business models to take advantage of machine learning.”

Business leaders, by and large, are on board with the potential benefits of AI, even as they are still warming up to the practical applications of it. An MIT Sloan Management Review study on the impacts of AI on business found that while three-quarters of executives believe AI will enable their companies to move into new businesses, and 85% believe AI will allow their companies to obtain or sustain a competitive advantage, only about one in five companies have incorporated AI in some of their services or processes.

Why is adoption so low? In part because there is both confusion and hype around AI. Gartner puts machine learning at the apex of the Peak of Inflated Expectations in its 2017 Emerging Trends Hype Cycle, but also notes that AI “will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks; these will enable organizations with AI technologies to harness data in order to adapt to new situations and solve problems that no one has ever encountered previously.”

Most current investment in AI is coming from internal R&D divisions within cash-rich digital natives like Amazon, Baidu, and Google. Much of the pickup outside of these organizations is still in discussion or pilot phase. A survey conducted by ServiceNow of 500 CIOs across 25 industries found that only 3% are using machine learning and AI across their companies. Another 20% are using AI in some areas of the business, 26% are piloting projects, and 40% are still in the research and planning phase.

CIOs, AI, and Data

For CIOs already grappling with the tremendous disruption brought on by digital transformation, AI adds a new layer of complexity. The good news is that AI technology will greatly aid digital transformation; IDC predicts that 40% of digital transformation initiatives will be supported by machine learning and AI by 2019.

AI success hinges largely on data; the MIT study revealed large gaps between AI leaders and laggards. “One sizable difference is their approach to data,” reads the report. “While most leaders are investing in AI talent and have built robust information infrastructures, other companies lack analytics expertise and easy access to their data.”

In a recent blog post titled AI for Data Management, Roger Nolan, Senior Director of Solutions at Informatica, explains that a key to machine learning processes is the quality of data with which the AI itself is trained. And, he notes, it’s not just the data itself, but the metadata around that data, that is crucial:

In the world of data management, the best source of training sets comes from metadata. The more metadata, the more patterns for AI/ML to use to observe that can result in intelligent recommendations, automatic detection of risky behavior, or complete automation of time-consuming or repetitive tasks. In summary, metadata is data about your data; what types of data, how it was accessed, the business meaning and context, when it was loaded or refreshed, how it is tagged, how it has been used and by who, which data tables have been joined, and much more. All this data provides a wealth of information for AI/ML to make suggestions and recommendations that will boost productivity of users.

The issue is that the intelligence component of AI is only as good as its underlying data. For AI to be not only accurate, but also meaningful, high-quality data is imperative. And unless there are procedures and technology in place to improve the quality of data being used to support AI—ensuring timely, accurate, trustworthy data—AI projects won’t live up to expectations.

Get Your Data in Order

It’s important to note that CIOs won’t necessarily be “buying” AI applications. Rather, AI will increasingly be a function of the software and services enterprises use to run their business. Incorporating machine learning techniques into platforms, products, and services will automate manual functions, accelerate analysis, and improve overall performance.

Those inherent benefits of machine learning extend to data management itself. As Informatica notes in a white paper, Artificial Intelligence for Data-Driven Disruption, machine learning techniques can be used to “teach” data management tools to make intelligent recommendations and automate many data management tasks.

“Machine learning does not replace data analysts and other users; instead, it is key to increasing the productivity and effectiveness of the data management staff within an organization,” the paper notes.

The CIO’s Role in AI Readiness

As a CIO, getting your data in order is one important step in preparing your organization for the successful adoption of AI. But two other steps are important as well:

Educate leadership on the myths and realities of AI. The pace of innovation in AI has been staggering. Gartner's advice to CIOs: Now is the perfect opportunity to educate your CEO and board – and the workforce at large, for that matter – about recent developments in AI and how those developments might influence your business and its competitive positioning.

Put the right skills in place to train AI algorithms. By automating routine tasks, AI can free up workers to focus on high-value activities. Some of those activities will involve training machine learning algorithms to do their jobs correctly. The MIT report found that “generating business value from AI is directly connected to effective training of AI algorithms”— generally by using specific company data. “Successful training depends on having well-developed information systems that can pull together relevant training data.” Build or acquire the tools necessary to ensure quality data. Then train your team to use them correctly. And relentlessly.

As data-driven digital transformation continues to disrupt industries, CIOs are becoming agents of change within their organizations. A data strategy that enables an organization to rapidly adopt AI and machine learning technologies will accelerate the pace of change and drive competitive differentiation.